Parallel Detrended Fluctuation Analysis for Fast Event Detection on Massive PMU Data

Khan, Muhammad Shahid; Ashton, Phillip M.; Li, Maozhen; Taylor, Gareth; Liu, Junyong

doi:10.1109/tsg.2014.2340446

Cited by 46 publications

(4 citation statements)

References 33 publications

(32 reference statements)

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“…Several factors bring this on, including task launch overhead, network bandwidth, and cluster communication overhead. There has been a lot of discussion about this phenomenon [72].…”

Section: Performance Evaluation Using Replicated Datasetmentioning

confidence: 99%

Optimized Feature Learning for Anti-Inflammatory Peptide Prediction Using Parallel Distributed Computing

Khan

et al. 2023

Applied Sciences

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With recent advancements in computational biology, high throughput Next-Generation Sequencing (NGS) has become a de facto standard technology for gene expression studies, including DNAs, RNAs, and proteins; however, it generates several millions of sequences in a single run. Moreover, the raw sequencing datasets are increasing exponentially, doubling in size every 18 months, leading to a big data issue in computational biology. Moreover, inflammatory illnesses and boosting immune function have recently attracted a lot of attention, yet accurate recognition of Anti-Inflammatory Peptides (AIPs) through a biological process is time-consuming as therapeutic agents for inflammatory-related diseases. Similarly, precise classification of these AIPs is challenging for traditional technology and conventional machine learning algorithms. Parallel and distributed computing models and deep neural networks have become major computing platforms for big data analytics now required in computational biology. This study proposes an efficient high-throughput anti-inflammatory peptide predictor based on a parallel deep neural network model. The model performance is extensively evaluated regarding performance measurement parameters such as accuracy, efficiency, scalability, and speedup in sequential and distributed environments. The encoding sequence data were balanced using the SMOTETomek approach, resulting in a high-accuracy performance. The parallel deep neural network demonstrated high speed up and scalability compared to other traditional classification algorithms study’s outcome could promote a parallel-based model for predicting anti-Inflammatory Peptides.

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“…Several factors bring this on, including task launch overhead, network bandwidth, and cluster communication overhead. There has been a lot of discussion about this phenomenon [72].…”

Section: Performance Evaluation Using Replicated Datasetmentioning

confidence: 99%

Optimized Feature Learning for Anti-Inflammatory Peptide Prediction Using Parallel Distributed Computing

Khan

et al. 2023

Applied Sciences

Self Cite

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“…Complex cascading events were detected using the same method. For the extraction of discriminative features, the minimum volume closing ellipsoid (MVEE) approach was used (Rafferty et al, 2017;Dahal et al, 2014), whereas, for event classification, the agglomerative hierarchical clustering method was applied (Khan et al, 2015). Some of the approaches used for automated event detection (Biswal et al, 2016;Brahma et al, 2017) include fast variation of the discrete Stransform (Biswal et al, 2016) and signal energy transform (Yadav et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Training Machine Learning Models with Simulated Data for Improved Line Fault Events Classification From 3-Phase PMU Field Recordings

Otudi,

Mohamed,

Kezunovic

et al. 2023

Proceedings of the Annual Hawaii International Conference on System Sciences

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Utilizing data analytics and machine learning (ML) on phasor measurement units (PMUs) data to analyze faults automatically is the focus of this paper. Insufficient labels and natural uneven distribution of different types of line fault events found in fieldrecorded PMU data make supervised ML model development challenging. To address this issue, we train off-the-shelf Support Vector Machine (SVM) ML models for line fault classification using simulated PMU data obtained from a combination of 12 physical and virtual PMUs placed on a synthetic IEEE 14-bus system as well as using this simulated data unified with field recordings. A conducted sensitivity study is focused on three factors, 1) the number of PMUs used to train the ML model, 2) the voltage level at which the model is trained, and 3) the vicinity of PMUs to transmission line faults. The ML models trained with simulated and field data are evaluated on one-year field-recorded data collected from 38 PMUs sparsely located in the US Western interconnection. We demonstrate that when training ML models with only simulated data, the performance varies significantly with different number of PMUs, voltage level, and PMU placement in separate areas of the synthetic grid (F1 score of 0.78 to 0.92). We obtained an F1 score of 0.94 using the simulated dataset integrated with field recordings. The performance of a ML model developed using simulated data is also evaluated on the three-phase voltage signals extracted from 188 PMUs in the Eastern interconnection accompanied by imprecise labels, where the majority of the labels do not identify the fault type. On this extremely challenging task, we achieved 77% accuracy solely using synthetic data for ML training.

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“…Moreover, in the field of electricity transmission, a high degree of operation safety is required. The characteristics of each considered microgrid determine a series of specific technical problems [7]. When considering the grid operation criteria and the specific needs of the users, the settings of the smart recloser equipment must be properly configured.…”

Section: Introductionmentioning

confidence: 99%

Unconventional Backup Structures Used in Smart Microgrids

Pîslaru‐Dănescu¹,

Lipan²

2018

Smart Microgrids

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The continuity of power supply to users is considered to be one of the main problems in the design and implementation of low-voltage smart microgrid configurations. Switching to the backup power supply, when using two frequency converters, one of which is alternately maintained in cold reserve, is presented. Switching to the backup power supply, in the case of low-voltage symmetrical smart microgrids, is another highlighted aspect. In the case of modern residential buildings, the automatic switching is necessary between two or more types of users, critical and noncritical ones to the available sources, like the public grid, photovoltaic panels, power generator, etc. Also, in this study, the implementation of smart power microgrids, featuring auto-reconfiguration, is proposed. It is considered the conversion of the public grids to active (distribution/using) smart power microgrids, which have the autoconfiguration option and use high-tech smart devices, like recloser type. Thus, the faults and contingencies will be limited or even removed, creating the frame for the supplied equipment (in a continuously increasing number due to the local and regional expansion) to operate until the removal of the fault.

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Parallel Detrended Fluctuation Analysis for Fast Event Detection on Massive PMU Data

Cited by 46 publications

References 33 publications

Optimized Feature Learning for Anti-Inflammatory Peptide Prediction Using Parallel Distributed Computing

Optimized Feature Learning for Anti-Inflammatory Peptide Prediction Using Parallel Distributed Computing

Training Machine Learning Models with Simulated Data for Improved Line Fault Events Classification From 3-Phase PMU Field Recordings

Unconventional Backup Structures Used in Smart Microgrids

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